Due to the undesirable behaviors associated with software robots, many internet services may require CAPTCHAs (Completely Automated Public Tests to Tell Computers and Humans Apart) for security purposes. However, sufficiently advanced computer programs continue to evolve and they may decode a number of captcha images that have been proposed to date. In light of the limitations associated with the existing CAPTCHAs, there is a need for assuring human interactions via captcha images differentiating human users from automated bots.
Aspects described herein may address these and other problems, and generally improve the accuracy, efficiency, and speed of generating captcha images via Generative Adversarial Network (GAN) model, Auto-Encoders (AE), or Variational Auto-Encoders (VAE), and by offering improved model training, increasing the probability that the human may recognize these captcha images, while abating the probability that the computer programs may decode the same images.